MadryLab / DebuggableDeepNetworks
☆37Updated 3 years ago
Related projects ⓘ
Alternatives and complementary repositories for DebuggableDeepNetworks
- ☆55Updated 4 years ago
- Do input gradients highlight discriminative features? [NeurIPS 2021] (https://arxiv.org/abs/2102.12781)☆13Updated last year
- ☆61Updated 3 years ago
- Source code of "Hold me tight! Influence of discriminative features on deep network boundaries"☆22Updated 2 years ago
- CVPR'19 experiments with (on-manifold) adversarial examples.☆44Updated 4 years ago
- Official code for "In Search of Robust Measures of Generalization" (NeurIPS 2020)☆28Updated 3 years ago
- Towards Understanding Sharpness-Aware Minimization [ICML 2022]☆35Updated 2 years ago
- ICLR 2021, Fair Mixup: Fairness via Interpolation☆55Updated 3 years ago
- On the effectiveness of adversarial training against common corruptions [UAI 2022]☆30Updated 2 years ago
- A modern look at the relationship between sharpness and generalization [ICML 2023]☆43Updated last year
- Code for the CVPR 2021 paper: Understanding Failures of Deep Networks via Robust Feature Extraction☆35Updated 2 years ago
- Code for the paper "Understanding Generalization through Visualizations"☆60Updated 3 years ago
- Official implementation for Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds (NeurIPS, 2021).☆22Updated 2 years ago
- [ICML'20] Multi Steepest Descent (MSD) for robustness against the union of multiple perturbation models.☆25Updated 3 months ago
- ☆35Updated last year
- The Full Spectrum of Deepnet Hessians at Scale: Dynamics with SGD Training and Sample Size☆17Updated 5 years ago
- ☆33Updated 3 years ago
- On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them [NeurIPS 2020]☆35Updated 3 years ago
- ☆19Updated 4 years ago
- The Pitfalls of Simplicity Bias in Neural Networks [NeurIPS 2020] (http://arxiv.org/abs/2006.07710v2)☆39Updated 10 months ago
- ☆17Updated 2 years ago
- Learning perturbation sets for robust machine learning☆64Updated 3 years ago
- ☆34Updated 3 years ago
- Code for the paper "Adversarial Training and Robustness for Multiple Perturbations", NeurIPS 2019☆46Updated last year
- Simple data balancing baselines for worst-group-accuracy benchmarks.☆40Updated last year
- Gradient-based Hyperparameter Optimization Over Long Horizons☆12Updated 3 years ago
- Code for "Neuron Shapley: Discovering the Responsible Neurons"☆23Updated 6 months ago
- Gradient Starvation: A Learning Proclivity in Neural Networks☆60Updated 3 years ago
- Code for "Just Train Twice: Improving Group Robustness without Training Group Information"☆67Updated 6 months ago
- Official repo for the paper "Make Some Noise: Reliable and Efficient Single-Step Adversarial Training" (https://arxiv.org/abs/2202.01181)☆25Updated 2 years ago